Mayank Sethi
Aug 18, 2018 · 4 min read

Tell us, what’s the one contradiction that defines the whole inventory management supply? It’s about having enough stock in the warehouse to keep the business running but not enough to drain the cash reserves. And managing this contradiction is the nightmare of any store manager. Even if a single item goes out of stock, the whole business can come trickling down. Remember when Tesla’s entire production had come to a standstill because one of its suppliers failed to deliver USB cables? Talk about how $3.00 item can threaten a billion-dollar company.

So how does one manage this messy, fast-paced world of inventory management using Artificial Intelligence? While we answer this question, a good thing is that today industries are more welcoming of technological transformation. There is no dearth of data and with Artificial Intelligence in play, there’ll be ample opportunities to analyze data and formulate strategies based on real-time insights.

1.Automated Inventory Management — AI eliminates the need to check inventory manually. With the power of predictive analysis, AI based systems can keep a track of the inventory stock. This enables companies to keep a track of total purchase orders and optimize the inventory. In case of a drop in the inventory, the AI system can automatically send out a repurchase order without any manual interference.

VERDIS enables supply chain managers to get ONE view of inventory across the supply chain. Besides, SCM managers can set alerts for specific levels (like safety stock levels, delivery lead times) and get alerts when these are getting breached. Artificial intelligence not only allows VERDIS to make predictions but also learn from user’s feedback which acts like a reward and punishment. If feedback is consistently given, it can result in improved functional capabilities and cost reductions of up to 32% across the operation

2. Demand Planning and Forecasting — Let’s admit this — machines can do some tasks better than humans, especially planning and forecasting inventory in complex supply chains. Inventory planning involves predicting future demand and analyzing several aspects that impact this demand. While humans might be a breed of fortune tellers, forecasting is a different ball game altogether. However, that’s not the case with Artificial Intelligence. AI techniques are algorithm-based which means that they can deliver accurate forecasts, with none or minimal error, even in cases where there is no historical data.

VERDIS can forecast things like inventory mark downs and stock outs. What’s more, it can master dynamic pattern matching, factoring in past, current and predicted events, and not rely just on historical information.

3. Order Picking — Order picking has grown to become the largest component of warehousing operations, especially after the tremendous growth of e-commerce. Keeping this in mind, it is ideal for warehouse managers to automate the process of picking. AI enabled systems can control and optimize conveyor speed to avoid queuing while picking as well as assign workers to specified zones where goods are being picked. Again, AI being a self-learning technology does not require any training. The net result is improved productivity, and saving in labor costs, which constitute 65% of most warehouse facilities’ operating budgets.

4. Data Mining and Sharing — We live in a world that’s more connected than ever before. This makes it easier to collect and share data. And this data fuels the artificial intelligence mechanism, making it easier to measure almost everything. Advanced data and analytics tools that run as a part of the AI ecosystem can help us understand how different parts of an operation interact with one another.

For inventory management, this could mean having all the data about inventory stock at our fingertips — place of origin, transit schedule, scanning times or its location and status that can be reported using radio frequency tags. All the information coming together in a centralized system give organizations the power to predict operations based on data. For instance, one can predict stock arrival times and whether there are any expected disruptions that might lead to a delay and in case of such disruptions therefore have the options to take remedial actions ahead of possible customer inconvenience.

VERDIS automatically provides insights to the functional role holders on any aspect which will impact their performance. Apart from operational aspects which may even be latent (happening deep inside an organization’s operations whose impact is not immediately visible), this also includes weather as well as political events. VERDIS thus helps to reduce dependency and streamline human operations and ultimately in improving customer relationships with the brand.

Inventory management is no more a trial and error game. With millions riding on stock availability and on-time product delivery, it has become imperative to stay on top of the game at all times. For business owners, the solution is in the form of systems like VERDIS.

Verdis | Prediction of Supply Chain Events

The official blog of Verdis.ai is about making supply chains more intelligent

Mayank Sethi

Written by

Business Analyst by Profession. Capability to Analyse, Evaluate and manage business with unique mind set. My passion is to heal businesses suffering from loss.

Verdis | Prediction of Supply Chain Events

The official blog of Verdis.ai is about making supply chains more intelligent

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